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830 results for “Industrialization”
Data Set Used in Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS
<p>This document contains the data set used for the study Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS that is currently in submission.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 6. Importance of AI for respondents business or industry by respondent's age
<p>On question 8 respondents have to indicate on a scale of 1 to 5 (1 being “not important” and 5 being “critical for survival”), how important they think the AI will be for their business or industry (or for the one they are preparing for) in the next 10 years? No statistical interaction was found between age and gender F (3, 108) = .174, p>0.05). There were statistically significant differences by gender F (1, 108) = 50.261, p<0.05 and age, F (3, 108) = 9.298, p<0.05. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=3.36, SD=.88) on question 8 about the importance of AI for the fields of activity of the participants (or for those they are preparing for) in the next 10 years. At the same question: the 26-30 age group scored the highest followed by the 18-25 age group (M=3.80, SD=1.18); the 31-40 age group scored lower than the 18-25 age group (M=3.50, SD=.87); the 31-40 age group scored also lower than the 18-25 age group (M=3.50, SD=.51) (Figure 6).</p>
Survey and Interview Data from Mixed-Method Survey of Serverless Computing and Function-as-a-Service Software Development in Industrial Practice
<p>This dataset contains the almost-raw data resulting from two out of the three methods chosen by the researchers for their namesake study «A Mixed-Method Empirical Study of Function-as-a-Service Software Development in Industrial Practice». Among the files are web survey questions, anonymised survey results, and interview guidelines. We encourage other researchers to perform open coding and other analysis techniques on the data to verify our claims and to generate new insights.</p>
Dataset of the article: "Technology validation of photosynthetic biogas upgrading in a semi-industrial scale algal-bacterial photobioreactor".
<p>Excel document that contains the data of the article: ‘Technology validation of photosynthetic biogas upgrading in a semi-industrial scale algal-bacterial photobioreactor’. This dataset shows the values obtained during the experimental period and it complements the corresponding article.</p>
A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants
<p>This folder contain the data relative to the contact based pipe inspection presented on M. Tognon et al. "A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants." IEEE Robotics and Automation Letters 4.2 (2019): 1846-1851.</p>
References to Qing China Technology and Industry in Finnish Technical Journals 1880-1912
<p>Dataset providing references to the Finnish and Swedish technological journal articles 1880-1912 used for analysis in the peer-reviewed journal article: Mats Fridlund & Matti La Mela, ”Between Technological Nostalgia and Engineering Imperialism: Digital History Readings of China in the Finnish Technoindustrial Public Sphere 1880-1912”, <em>Tekniikan Waiheita</em> 37 (2019):1"</p>
MADFORWATER: WP2: Adaptation of wastewater treatment technologies for agricultural reuse: Task2.4: Industrial wastewater treatment: Treatment of different types of wastewater by means of innovative resins: Subset5
<p>This dataset contains the data underlying the following publication: Yu Huang, Shi Cheng, Ya-Ping Wu, Ji Wu<sup> </sup>, Yan Li, Zong-Li Huo, Ji-Chun Wu<sub>­</sub>, Xian-Chuan Xie, Gregory V. Korshin , Ai-Min Li<sup> </sup>, Wen-Tao Li (2019)</p> <p>Developing surrogate indicators for predicting suppression of halophenols formation potential and abatement of estrogenic activity during ozonation of water and wastewater. <em>Water Research.</em> <a href="https://doi.org/10.1016/j.watres.2019.05.092">https://doi.org/10.1016/j.watres.2019.05.092</a></p>
Software Variability Tools: Industrial Survey and Systematic Mapping Study
<p>Software Variability Tools: Industrial Survey and Systematic Mapping Study</p>
Industry-scale Application and Evaluation of Deep Learning for Drug Target Prediction
<p>Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.</p>
Figure 1 in Using of fluctuating asymmetry in adult Pelophylax ridibundus (Amphibia: Anura: Ranidae) meristic traits as a method for assessing developmental stability of population and environmental quality of their habitat: industrial area in southern Bulgaria
Figure 1. An indicative map of the sites in southern Bulgaria where P. ridibundus individuals were captured in 2019.
Figure 2 in Using of fluctuating asymmetry in adult Pelophylax ridibundus (Amphibia: Anura: Ranidae) meristic traits as a method for assessing developmental stability of population and environmental quality of their habitat: industrial area in southern Bulgaria
Figure 2. Photos of some asymmetric P. ridibundus individuals from site 1: the Chaya River in southern Bulgaria. Legend: a–d: asymmetric morphological traits on the back of the body and hind limbs of frogs, e–f: asymmetric morphological traits on the fingers of frogs. Trait 1 – number of stripes on the dorsal side of the thigh (femur); trait 2 – number of spots on the dorsal side of the thigh; trait 3 – number of stripes on the dorsal side of the shank (crus); trait 4 – number of spots on the dorsal side of the shank; trait 5 – number of stripes on the foot (pes); trait 6 – number of spots on the foot; trait 7 – number of stripes and spots on the back (dorsum); trait 8 – number of white spots on the ventral side of the second finger of the hind leg; trait 9 – number of white spots on the ventral side of the third finger of the hind leg; trait 10 – number of white spots on the ventral side of the fourth finger of the hind leg.
Industrial Big Data Innovation Platform SCADA Dataset
<p>This is the SCADA dataset from the first Industrial Big Data Innovation Competition, for more information, visit: <a href="https://www.industrial-bigdata.com/Challenge/title?competitionId=LEIREZMM8TT5VBU0TLJ61FPAI6WWJOJY&type=">数境创新大赛平台(industrial-bigdata.com)。</a></p>
Production of hydrochar fuel by microwave-hydrothermal carbonisation of olive pomace slurry from olive oil industry for combustion application
<p><span>Extended dataset for research article titled “Production of hydrochar fuel by microwave-hydrothermal carbonisation of olive pomace slurry from olive oil industry for combustion application.” It includes the following data files: </span></p> <p><span>(1) Dataset description.txt (provides abbreviations or codes of samples and their properties); Checksum MD5 code = a2fc3f46e9d3ef61f390f93f4a45fa97 </span></p> <p><span>(2) Extended dataset v1.0.xlsx (data files for the biochemical, proximate, ultimate, HHV, and mineral characteristics of raw material (two-phase olive pomace slurry) and hydrochars; Checksum MD5 code = 01469e10c1133367d41c5ab5553a9616</span></p> <p><span>(3) 13C Solid NMR data.zip (raw data files for 13C-solid nuclear magnetic resonance analysis of raw material and hydrochars); Checksum MD5 code = dd446a11e92cef9d63ee5e2f5786ee7c</span></p> <p><span>(4) TGA-DTA data.zip (raw data for thermal gravimetric analysis of raw material and hydrochars); Checksum MD5 code = 0bb2a59e6cc3c2b3de0492ac057fa626</span></p> <p><span>(5) FTIR data.zip (raw data for fourier transform infrared analysis of raw material and hydrochars); Checksum MD5 code = ff20a5545198ec7b395506ccdd841e82</span></p> <p> </p> <p> </p>
Next-Gen Quality Assurance: A Deep Dive into Integrating Artificial Intelligence in the Pharmaceutical Industry
<p>This article explores the transformative<br>integration of Artificial Intelligence (AI) with<br>advanced quality tools in the pharmaceutical<br>industry. From the adoption of Analytical<br>Quality by Design (AQbD) principles in<br>method development to the application of<br>AI in rapid testing, Design of Experiments<br>(DoE), and statistical tools for trending and<br>process capability analysis, a synergistic<br>relationship emerges. The amalgamation of<br>AI and advanced tools represents a<br>paradigm shift, propelling quality assurance<br>into a new frontier marked by precision,<br>reliability, and excellence in pharmaceutical<br>manufacturing. Despite encountering<br>challenges such as data privacy and<br>regulatory compliance, the industry is<br>moving towards a future where AI is not<br>merely a tool but a strategic partner, shaping<br>a revolutionary landscape where the highest<br>standards of quality are not only met but<br>exceeded.</p>
Replication Package for a Systematic Literature Mapping of Agility in Safety-Critical Software Development within the Aerospace Industry
<p>This file collection package facilitates the replication of a Systematic Literature Mapping (SLM) focused on Agility in Safety-Critical Software Development within the Aerospace Industry. Authored by J. Eduardo Ferreira Ribeiro, João Gabriel Silva, and Ademar Aguiar, this dataset is dedicated to improving transparency and reproducibility in this field of study and future research.</p> <p>Specifically, the package includes:</p> <ul> <li><a href="https://github.com/zemacedo99/Replication-Package-Builder/releases/tag/v1.0.2">Replication Package Builder Version 1.0.2</a></li> <li>A list of terms (both inclusion and exclusion) used to construct the research string.</li> <li>A list of venues unrelated to the research topic, to be excluded from the results.</li> <li>The inclusion and exclusion criteria applied during the study.</li> <li>Lists of publication results from indexing services like Scopus, IEEE Xplore, Science Direct, HAL Open Science, Springer Nature, and the ACM Digital Library are all provided in CSV file format.</li> <li>A list of all publications in CSV format, compiled after the automated exclusion phase using the established inclusion and exclusion criteria.</li> <li>Finally, a complete list of all publications, including those from Snowball sampling, in XLSX format was compiled after the manual exclusion phase using the established inclusion and exclusion criteria.</li> </ul> <p>Compiled and published on Saturday, September 14, 2024, this dataset is crucial for researchers seeking to replicate or extend the SLM's findings.</p> <p>Lastly, we thank J. Antonio Dantas Macedo for contributing to developing and providing this <a href="https://github.com/zemacedo99/Replication-Package-Builder">replication package builder</a>.</p>
IAM_COMPACT_Study_4_EU_Industry
<p>This dataset contains the underling raw data of IAM COMPACT "Study 4 - EU Industry".</p> <p>The study started from stakeholders’ questions around relocation of European Industries and associated impacts on costs, sustainability and labour and condensed these into two sub-studies:</p> <ol> <li>The first one analysed the European steel industry and a potential relocation thereof to other world regions because of high energy and CO2 prices in the EU.</li> <li>The second one addressed – in the context of potential re-shoring of critical net-zero technologies to the EU – the raw material demand for upscaling solar and wind technologies in four global regions, including the EU (section 3 of D4.7 (Holtz et al., 2023)).</li> </ol> <p>The first sub-study on the EU steel industry involved the soft-linking of several models to assess the potential impact of different degrees of trade restrictions for steel. Three scenarios are analysed in each of which all countries implement their current climate policies and achieve the GHG emissions reduction targets set in their respective NDCs and in their long-term targets, but different steel-related trade constraints are assumed in the three scenarios:</p> <ul> <li>“NDC_LTT” scenario: no trade restriction</li> <li>“CBAM” scenario: steel imports to the EU are penalized based on their CO2 footprint</li> <li>“INDEPENDENCE” scenario: steel production levels in the EU may not drop below the level of 2019 (which is guaranteed by subsidies provided for steel production)</li> </ul> <p>The second sub-study on raw material demand for upscaling solar and wind technologies builds on the three scenarios of the steel-related study (see above). Results from GCAM on renewables upscaling were used as an input to some modules of WILIAM which allowed to calculate the raw materials demands implied, which were then compared to current annual extraction and the known global reserves.</p> <p>The GCAM model was used to develop scenarios for the steel production in different global regions considering trade and trade restrictions and all other model applications in this study built on these results. The three steel-related scenarios analysed with GCAM all build on the NDC_LTT scenario of study 1 used for the comparison of EU Fit-for-55 policy to a cost-optimal scenario (section 3 of D4.5 (Mittal et al., 2023)). Therefore, all policies included in the NDC_LTT scenario of Study 1 are implicitly included in Study 4 scenarios as well. However, these were not analysed specifically in Study 4. The policy measures explicitly addressed in Study 4 are related to steel trade. Furthermore, the CO2 price resulting from GCAM was used as an input by the WISEE EDM-I bottom-up steel model.</p> <div>Results of the study have been documented in D4.7 - Sectoral and cross-sectoral analysis (DOI <a href="../doi/10.5281/zenodo.13839232">10.5281/zenodo.13839232</a>)</div>
Dataset: A review of methods to trace material flows into final products in dynamic material flow analysis - from industry shipments in physical units to monetary input-output tables (p
<p>Dynamic material flow analysis (dMFA) is widely used to model stock-flow dynamics. To appropriately represent material lifetimes, recycling potentials, and service provision, dMFA requires data about the allocation of economy-wide material consumption to different end-use products or sectors, that is, the different product stocks, in which material consumption accumulates. Previous estimates of this allocation only cover few years, countries, and product groups. Recently, several new methods for estimating end-use product allocation in dMFA were proposed, which so far lack systematic comparison. We review and systematize five methods for tracing material consumption into end-use products in inflow-driven dMFA and discuss their strengths and limitations. Widely used data on industry shipments in physical units have low spatio-temporal coverage, which limits their applicability across countries and years. Monetary input–output tables (MIOTs) are widely available and their economy-wide coverage makes them a valuable source to approximate material end-uses. We find four distinct MIOT-based methods: consumption-based, waste input–output MFA (WIO-MFA), Ghosh absorbing Markov chain, and partial Ghosh. We show that when applied to a given MIOT, the methods’ underlying input–output models yield the same results, with the exception of the partial Ghosh method, which involves simplifications. For practical applications, the MIOT system boundary must be aligned to those of dMFA, which involves the removal of service flows, sector (dis)aggregation, and re-defining specific intermediate outputs as final demand. Theoretically, WIO-MFA, applied to a modified MIOT, produces the most accurate results as it excludes massless and waste transactions. In part 2 of this work, we compare methods empirically and suggest improvements for aligning MIOT-dMFA system boundaries.</p>
THE IMPACT OF LOCAL PRODUCT BRANDING ON THE ECONOMIC PERFORMANCE OF AGRICULTURAL AND LIVESTOCK AGRO-PROCESSING INDUSTRIES IN VLORA
<p>This study aims to examine the impact of local product branding on the economic performance of the agricultural and livestock agro-processing industries in the region of Vlora. Based on the data collected from local agro-processing and agro-tourism industries, the purpose of this research is to analyze how branding strategies contribute to increasing the level of sales, the level of income, creating a strong identity, and improving the performance of businesses and the territory where these businesses are concentrated. The study includes in the analysis the influence of the local brand in the creation of the identity, in the level of sales, in the income, the profit margins, and the improvement of the economic performance of the agricultural and livestock agro-processing industries. For this reason, the research was conducted with the inclusion of over 100 industries/agritourism, and the analysis of the questionnaire data was conducted with the STATA program. From the results of the research, it is clear that investment in the branding of products with local indicators is necessary to stimulate economic development and to strengthen the market positioning of businesses in the agricultural and livestock sectors. Also, this research provides important recommendations for improving branding practices as a strategic tool for the development and consolidation of agro-processing industries in the region.</p>
Linked collectors and determiners for: Colección Herpetológica (Reptiles) del Museo de Historia Natural de la Universidad Industrial de Santander.
Natural history specimen data linked to collectors and determiners held within, "Colección Herpetológica (Reptiles) del Museo de Historia Natural de la Universidad Industrial de Santander". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/c816ec3b-ed12-4891-b304-d4e626e43cb3">https://bionomia.net/dataset/c816ec3b-ed12-4891-b304-d4e626e43cb3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/c816ec3b-ed12-4891-b304-d4e626e43cb3">https://gbif.org/dataset/c816ec3b-ed12-4891-b304-d4e626e43cb3</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Herbario de la Universidad Industrial de Santander.
Natural history specimen data linked to collectors and determiners held within, "Herbario de la Universidad Industrial de Santander". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/cc5e1e3d-0850-4cde-a282-656657752e13">https://bionomia.net/dataset/cc5e1e3d-0850-4cde-a282-656657752e13</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/cc5e1e3d-0850-4cde-a282-656657752e13">https://gbif.org/dataset/cc5e1e3d-0850-4cde-a282-656657752e13</a>. Formatted as a Frictionless Data package.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.